Manager, Operational AI Strategy

LVT is looking for a strategic, hands-on leader to own our company-wide AI transformation. As Manager, Operational AI Strategy, you will define how LVT adopts and scales AI across every function — building the models, policies, and cross-functional partnerships that turn AI from an experiment into a competitive advantage. This role sits at the intersection of strategy and execution: you’ll architect our AI roadmap, develop internal AI policy, rationalize our AI tool investments, and directly build AI components within our automation workflows alongside our Integrations and Data Engineering teams.

LVT is a flexible-first company. This role is based in-office at our American Fork, UT headquarters.

Strategic and technical: You can write a boardroom-ready AI strategy in the morning and build a RAG pipeline in the afternoon — you’re equally comfortable in both modes.

AI practitioner: You have hands-on experience building and deploying LLM-based solutions, including retrieval-augmented generation (RAG), vector databases, and model fine-tuning or prompt engineering at scale.

Platform fluent: You understand enterprise automation platforms (Workato, OIC, or equivalent) and can reason clearly about where AI fits within complex integration flows.

Tech stack evaluator: You know how to assess embedded AI features across SaaS and enterprise tools — cutting through vendor marketing to determine what’s actually delivering value, what’s redundant, and what should go.

Policy-minded: You’ve developed or contributed to AI governance frameworks, usage policies, or organizational AI standards — and you understand the stakes of getting it right.

Cross-functional influencer: You build trust with engineering, operations, and executive stakeholders alike, and know how to move initiatives forward without direct authority over every team involved.

Data-environment comfortable: You’re experienced working within data platforms (e.g., Snowflake, Cortex) and partnering with data engineering teams to bring AI models from concept to production.

Outcome-oriented: You measure your work by business impact — adoption rates, efficiency gains, cost reduction — not by the sophistication of the model you shipped.